Introduction: LLM APIs fail in ways traditional APIs don’t—rate limits, content filters, malformed outputs, timeouts on long generations, and model-specific quirks. Building resilient LLM applications requires comprehensive error handling: retry logic with exponential backoff, fallback strategies when primary models fail, circuit breakers to prevent cascade failures, and graceful degradation for user-facing applications. This guide covers […]
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The Vibe Coding Revolution: How AI Assistants Are Redefining Developer Productivity in 2025
The term “vibe coding” emerged organically from developer communities in late 2024, describing a new paradigm where programmers collaborate with AI assistants not just for code completion, but for entire feature implementations. Workflow Revolution: Traditional vs Vibe Coding Understanding the Vibe Coding Paradigm Vibe coding represents a fundamental shift in how developers interact with their […]
Read more →Structured Generation Techniques: Getting Reliable JSON from LLMs
Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]
Read more →Multi-Modal LLM Integration: Building Applications with Vision Capabilities
Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building […]
Read more →LLM Rate Limiting and Throttling: Building Resilient AI Applications
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request caps. Hit these limits and your application grinds to a halt with 429 errors. Worse, aggressive retry logic can trigger longer cooldowns. Proper rate limiting isn’t just about staying under limits; it’s about maximizing throughput while gracefully handling bursts, prioritizing […]
Read more →LLM Request Batching: Maximizing Throughput with Parallel Processing
Introduction: Processing LLM requests one at a time is inefficient. When you have multiple independent requests, sequential processing wastes time waiting for each response before starting the next. Batching groups requests together for parallel processing, dramatically improving throughput. But batching LLM requests isn’t straightforward—you need to handle rate limits, manage concurrent connections, deal with partial […]
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